Neuware -Data clustering is a prevalent challenge in big data processing, and parallelizing clustering operations significantly enhances efficiency in applications involving frequent searches. Various clustering techniques are available for data grouping, with CBAR being widely used across different applications. Parallelizing CBAR is essential for big data, and the Hadoop MapReduce platform offers a suitable framework to improve efficiency by leveraging effective segmentation techniques. This book involves designing and implementing algorithms for CBAR using the MapReduce approach, with testing conducted on clusters of up to 4 nodes. The results demonstrate substantial performance gains, which are analyzed and discussed with illustrative examples.Books on Demand GmbH, �berseering 33, 22297 Hamburg 76 pp. Englisch.

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Title: Parallel CBAR Technique in Hadoop-MapReduce Framework
Publisher: LAP LAMBERT Academic Publishing Nov 2024
Publication Date: 2024
Language: English
ISBN 10: 3659912751
ISBN 13: 9783659912757
Binding: Taschenbuch
Condition: Neu
Dimensions: 220 millimeters width by 150 millimeters height by 5 millimeters depth
Weight: 131 grams

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Data clustering is a prevalent challenge in big data processing, and parallelizing clustering operations significantly enhances efficiency in applications involving frequent searches. Various clustering techniques are available for data grouping, with CBAR being widely used across different applications. Parallelizing CBAR is essential for big data, and the Hadoop MapReduce platform offers a suitable framework to improve efficiency by leveraging effective segmentation techniques. This book involves designing and implementing algorithms for CBAR using the MapReduce approach, with testing conducted on clusters of up to 4 nodes. The results demonstrate substantial performance gains, which are analyzed and discussed with illustrative examples.

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